1. Field of the Invention
This invention relates in general to cardiac monitoring and in particular to estimation of ventricular stroke volume variation (SVV) as well as to a system that implements the method.
2. Background Art
Stroke volume (SV), cardiac output (CO), etc., are important indicators not only for diagnosis of disease, but also for “real-time” monitoring of the condition of both human and animal subjects, including patients. Few hospitals are therefore without some form of equipment to monitor one or more of these cardiac parameters. Many techniques—invasive and non-invasive, as well as those that combine both—are in use and even more have been proposed in the literature.
Most of the techniques used to measure SV can usually be readily adapted to provide an estimate of CO as well, since CO is generally defined as SV times the heart rate HR, which is usually available to monitoring equipment. Conversely, most devices that estimate CO also estimate SV as a sub-step.
As is explained in greater detail below, still another cardiac parameter that promises to provide clinically important information is stroke volume variation SVV. One way to estimate SVV is simply to collect multiple SV values and calculate the differences from measurement interval to measurement interval.
One common way to measure SV or CO is to mount some flow-measuring device on a catheter, and then to thread the catheter into the subject and to maneuver it so that the device is in or near the subject's heart. Some such devices inject either a bolus of material or energy (usually heat) at an upstream position, such as in the right atrium, and determine flow based on the characteristics of the injected material or energy at a downstream position, such as in the pulmonary artery. Patents that disclose implementations of such invasive techniques (in particular, thermodilution) include:
U.S. Pat. No. 4,236,527 (Newbower et al., 2 Dec. 1980);
U.S. Pat. No. 4,507,974 (Yelderman, 2 Apr. 1985);
U.S. Pat. No. 5,146,414 (McKown, et al., 8 Sep. 1992); and
U.S. Pat. No. 5,687,733 (McKown, et al., 18 Nov. 1997).
Still other invasive devices are based on the known Fick technique, according to which CO is calculated as a function of oxygenation of arterial and mixed venous blood.
Invasive techniques have obvious disadvantages, the main one of which is of course that catheterization of the heart is potentially dangerous, especially considering that the subjects (especially intensive care patients) on which it is performed are often already in the hospital because of some actually or potentially serious condition. Invasive methods also have less obvious disadvantages: Some techniques such as thermodilution rely on assumptions, such as uniform dispersion of the injected heat, that affect the accuracy of the measurements depending on how well they are fulfilled. Moreover, the very introduction of an instrument into the blood flow may affect the value (for example, flow rate) that the instrument measures.
Doppler techniques, using invasive as well as non-invasive transducers, are also used to measure flow and to calculate SV and CO from the flow measurements. Not only are these systems typically expensive, but their accuracy depends on precise knowledge of the diameter and general geometry of the flow channel. Such precise knowledge is, however, seldom possible, especially under conditions where real-time monitoring is desired.
There has therefore been a long-standing need for some way of determining cardiac parameters such as SV, SVV, etc., that is both non-invasive, or at most minimally invasive, and accurate. One blood characteristic that has proven particularly promising for accurately determining such parameters with minimal or no invasion is blood pressure.
Most known blood-pressure-based systems rely on the so-called pulse contour method (PCM), which calculates an estimate of the cardiac parameter(s) of interest from characteristics of the beat-to-beat pressure waveform. In the PCM, “Windkessel” (German for “air chamber”) parameters (characteristic impedance of the aorta, compliance, and total peripheral resistance) are typically used to construct a linear or non-linear, hemodynamic model of the aorta. In essence, blood flow is analogized to a flow of electrical current in a circuit in which an impedance is in series with a parallel-connected resistance and capacitance (compliance). The three required parameters of the model are usually determined either empirically, through a complex calibration process, or from compiled “anthropometric” data, that is, data about the age, sex, height, weight, etc., of other patients or test subjects. U.S. Pat. No. 5,400,793 (Wesseling, 28 Mar. 1995) and U.S. Pat. No. 5,535,753 (Petrucelli,.et al., 16 Jul. 1996) are representative of systems that rely on a Windkessel circuit model to determine CO.
PCM-based systems can monitor SV-derived cardiac parameters more or less continuously, with no need for a catheter (usually right heart) to be left in the patient. Indeed, some PCM systems operate using blood pressure measurements taken using a finger cuff. One drawback of PCM, however, is that it is no more accurate than the rather simple, three-parameter model from which it is derived; in general, a model of a much higher order would be needed to faithfully account for other phenomena, such as the complex pattern of pressure wave reflections due to multiple impedance mis-matches caused by, for example, arterial branching. Because the accuracy of the basic model is usually not good enough, many improvements have been proposed, with varying degrees of complexity
The “Method and apparatus for measuring cardiac output” disclosed by Salvatore Romano in U.S. Published Patent Application 20020022785 A1 (21 Feb. 2002, “Method and apparatus for measuring cardiac output”) represents a different attempt to improve upon PCM techniques by estimating SV, either invasively or non-invasively, as a function of the ratio between the area under the entire pressure curve and a linear combination of various components of impedance. In attempting to account for pressure reflections, the Romano system relies not only on accurate estimates of inherently noisy derivatives of the pressure function, but also on a series of empirically determined, numerical adjustments to a mean pressure value.
Fluid administration in hemodynamically unstable patients is often a major challenge when it-comes to measuring SV, CO, or other hemodynamic parameters in real time. Correct clinical assessment of hypovolemia is difficult, as is the decision to undertake fluid resuscitation as the initial treatment strategy. Specifically, it is very difficult to predict whether a hemodynamically unstable patient will respond to fluid therapy with an increase in stroke volume and cardiac output. Moreover, fluid overload can cause significant pulmonary or cardiac dysfunction, whereas fluid insufficiency may cause tissue damage resulting in vital organ dysfunction. A patient's fluid responsiveness is the major and most important determinant to assess the adequacy of fluid resuscitation therapy and to ensure optimal cardiac performance and organ  perfusion.
Many bedside indicators of ventricular preload have been used as predictors of fluid responsiveness. Right arterial pressure (RAP) and pulmonary artery occlusion pressure (PAOP) are the most commonly used in the intensive care unit (ICU) when deciding to administer fluids. Other bedside indicators of ventricular preload include right ventricular end diastolic volume (RVEDV) and left ventricular end diastolic area (LVEDA) measured with transesophageal echocardiography. Several studies and case reports have shown, however, that these static indicators based on cardiac filling pressures have poor predictive value and often fail to give adequate information about fluid responsiveness.
Recently, several studies have confirmed the clinical significance of monitoring the variations observed in left ventricular stroke volume that result from the interaction of the cardiovascular system and the lungs under mechanical ventilation. These stroke volume variations (SVV) are caused by the cyclic increases and decreases in the intrathoracic pressure due to the mechanical ventilation, which lead to variations in the cardiac preload and afterload. SVV has recently been extensively investigated and several studies have shown the usefulness of using SVV as predictor of fluid responsiveness in various clinical situations. Several other parameters based on SVV have been found to be useful as well. In particular, systolic pressure variation (SPV) with its delta-Up (ΔUp) and delta-Down (ΔDown) components has been found to be a very useful predictor of fluid responsiveness. SPV is based on the changes in the arterial pulse pressure due to respiration-induced variations in stroke volume. Yet another parameter that has recently been investigated and shown to be a valid indicator of fluid responsiveness is the pulse pressure variation (PPV).
Recent developments in arterial pulse contour analysis methods have opened unique opportunities for less-invasive, continuous and real-time estimation of SVV. This allows clinicians to use SVV routinely along with SV and CO in their assessment of the hemodynamic state of critical care patients.
Existing systems for measuring fluid responsiveness based on respiration-induced changes in the arterial pulse pressure are almost all based on one of only a few methods. Some of the methods described in the literature include the following measurement of Pulse Pressure Variation (PPV), Systolic Pressure Variation (SPV) and Stroke Volume Variation (SVV).
PPV estimation is based on some version of the following Equation 1:PPV=100·[PPmax−PPmin)/[½(PPmax+PPmin)]  (Equation 1)where PP is measured pulse pressure, PPmax and PPmin are, respectively, the maximum and the minimum peak-to-peak values of the pulse pressure during one respiratory (inspiration-expiration) cycle.
SPV estimation is based on some version of the following Equation 2:SPV=100·[SPmax−SPmin)/[½(SPmax+SPmin)]  (Equation 2)where SP is measured systolic pressure, SPmax and SPmin are respectively the maximum and minimum values of the systolic pressure during one respiratory cycle.
Similarly, SVV estimation is based on some version of the following Equation 3:SVV=100·[SVmax−SVmin)/[½(SVmax+SVmin)]  (Equation 3)where SV is stroke volume, SVmax and SVmin are respectively the maximum and minimum values of the stroke volume during one respiratory cycle.
In Equations 1, 2, and 3, the denominators are the averages of the maximum and minimum values of PP, SP and SV, respectively. In other words, the denominators are mean values, albeit of only two measurement points. This simple averaging of extreme values has been most common merely to simplify the calculations, which have typically been performed by hand. More reliable values may be obtained, however, by using the mean of all the measurement values over the measurement interval, that is, the first statistical moment of PP, SP, and SV.
Thus, for each of PPV, SPV and SVV, the respective variation value formula expresses the magnitude of the range of the value (maximum minus minimum) relative to the mean of the extreme (maximum and minimum) values.
The specific monitoring of SVV has both specific difficulties and advantages. Physiologically, SVV is based on several complex mechanisms of cardio-respiratory interaction. In brief: mechanical ventilation causes changes in left ventricular preload, which leads to distinct variations in left ventricular stroke volume and systolic arterial pressure. Monitoring of SVV enables prediction of left ventricular response to volume administration and helps with correct assessment of hypovolemia and the subsequent decision to undertake volume resuscitation in many critical situations.
In addition to the three methods listed above, there is a fourth method, known as Perel's method, which is generally called the “respiratory systolic variation test.” This method involves airway pressure maneuvers, such as inducing tidal volumes of varying amplitudes preceded by a short apnea period. U.S. Pat. No. 5,769,082 (Perel) describes this method. Because of the requirement for pressure maneuvers, this method is not suitable for real-time monitoring.
The methods listed above are implemented in some CO monitoring instruments such as LiDCO Ltd.'s cardiac monitor and the PiCCO system of Pulsion Medical Systems (see, for example, U.S. Pat. No. 6,315,735—Joeken et al), both of which rely on Equation 3. Tests by the inventors show however, that these methods are so noisy that they do not allow for accurate real-time monitoring of the respiration-induced changes in the arterial pulse pressure. The main reason for the noise problems in the SVV estimation in those instruments is the way SV is calculated. For example, the PiCCO system uses a beat-to-beat stroke volume estimation method based on a pulse contour algorithm that involves detection of specific points in the blood pressure waveform, such as the dicrotic notch. Precise detection of the dicrotic notch and other points in the blood pressure signal, however, is difficult, due to the inconstancy of the blood pressure waveform and its volatile nature.
What is needed is therefore a system and method of operation for estimating SVV in real time more accurately and robustly than is now possible, using at most minimally invasive techniques. This invention meets this need.